{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import os\n",
    "import platform\n",
    "import pdb\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib.pyplot import cm\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('montreal2017.csv')\n",
    "data.replace('InVld',np.nan,inplace=True)\n",
    "data.replace('<Samp',np.nan,inplace=True)\n",
    "data.replace('Down',np.nan,inplace=True)\n",
    "data.replace('Calib',np.nan,inplace=True)\n",
    "data.replace('NoData',np.nan,inplace=True)\n",
    "dataset=data.iloc[:,4:].copy()\n",
    "dataset.to_csv('montreal.csv')\n",
    "dataset=pd.read_csv('montreal.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=dataset.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.96"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "99.96"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.iat[0,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(dataset)):\n",
    "    for j in range(len(dataset.columns)):\n",
    "        if np.isnan(dataset.iat[i,j]):\n",
    "            if i==0:\n",
    "                dataset.iloc[i,j]=dataset.iat[i+1,j]\n",
    "            elif i==len(data)-1:\n",
    "                dataset.iloc[i,j]=dataset.iat[i-1,j]\n",
    "            else:\n",
    "                dataset.iloc[i,j]=np.nanmean([dataset.iat[i-1,j],dataset.iat[i+1,j]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_value=torch.FloatTensor(dataset.min())\n",
    "max_value=torch.FloatTensor(dataset.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([  0.0000, -26.6000, -33.0000,  23.0000,   0.0000,   0.0000,\n",
      "          0.2000,  97.1700,   0.0000,   0.0000,   0.0000,   6.5000,\n",
      "          0.0000])\n",
      "tensor([ 8759.0000,    31.9000,    22.3000,    99.0000,    36.0000,\n",
      "           67.0000,    80.5000,   103.8800,    72.7000,    77.6000,\n",
      "           55.9000,   996.9000,    68.6000])\n",
      "tensor([  0.0000, -26.6000, -33.0000,  23.0000,   0.0000,   0.0000,\n",
      "          0.2000,  97.1700,   0.0000,   0.0000,   0.0000,   6.5000,\n",
      "          0.0000])\n",
      "tensor([ 8759.0000,    31.9000,    22.3000,    99.0000,    36.0000,\n",
      "           67.0000,    80.5000,   103.8800,    72.7000,    77.6000,\n",
      "           55.9000,   996.9000,    68.6000])\n"
     ]
    }
   ],
   "source": [
    "print(min_value)\n",
    "print(max_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = preprocessing.MinMaxScaler() \n",
    "scaled_values = scaler.fit_transform(dataset) \n",
    "# dataset.loc[:,:]=scaled_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_set=np.asarray(dataset,dtype=np.float32)\n",
    "# seq_len = 30 + 1\n",
    "# x=len(data_set)-seq_len\n",
    "# sequences = [data_set[t:t+seq_len] for t in range(x)]\n",
    "# seq=torch.FloatTensor(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_set=np.asarray(scaled_values,dtype=np.float32)\n",
    "seq_len=30+1\n",
    "x=len(input_set)-seq_len\n",
    "sequences=[input_set[t:t+seq_len] for t in range(x)]\n",
    "for i in range(len(sequences)):\n",
    "    sequences[i]=scaler.fit_transform(sequences[i])\n",
    "x_set=torch.FloatTensor(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8729, 31, 13])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([8729, 31, 13])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_row=round(0.90*x_set.size(0))\n",
    "x_train_set=x_set[:split_row, :-1]\n",
    "y_train_set=x_set[:split_row, -1]\n",
    "x_valid_set=x_set[split_row:, :-1]\n",
    "y_valid_set=x_set[split_row:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.0000,  0.4211,  0.5625,  ...,  0.8852,  1.0000,  1.0000],\n",
       "        [ 1.0000,  0.5132,  0.6719,  ...,  0.9124,  1.0000,  0.7360],\n",
       "        [ 1.0000,  0.5658,  0.7344,  ...,  1.0000,  0.7536,  0.3614],\n",
       "        ...,\n",
       "        [ 1.0000,  0.6444,  1.0000,  ...,  0.1932,  0.0000,  0.0000],\n",
       "        [ 1.0000,  0.8667,  1.0000,  ...,  0.0874,  0.0000,  0.0000],\n",
       "        [ 1.0000,  0.9535,  1.0000,  ...,  0.0291,  0.0000,  0.0000]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.0000,  0.4211,  0.5625,  ...,  0.8852,  1.0000,  1.0000],\n",
       "        [ 1.0000,  0.5132,  0.6719,  ...,  0.9124,  1.0000,  0.7360],\n",
       "        [ 1.0000,  0.5658,  0.7344,  ...,  1.0000,  0.7536,  0.3614],\n",
       "        ...,\n",
       "        [ 1.0000,  0.6444,  1.0000,  ...,  0.1932,  0.0000,  0.0000],\n",
       "        [ 1.0000,  0.8667,  1.0000,  ...,  0.0874,  0.0000,  0.0000],\n",
       "        [ 1.0000,  0.9535,  1.0000,  ...,  0.0291,  0.0000,  0.0000]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self,input_size,hidden_size,num_layers=2,dropout=0,bidirectional=False):\n",
    "        super(LSTM,self).__init__()\n",
    "        self.input_size=input_size\n",
    "        self.hidden_size=hidden_size\n",
    "        self.num_layers=num_layers\n",
    "        self.dropout=dropout\n",
    "        self.bidirectional=bidirectional\n",
    "        self.lstm = nn.LSTM(input_size,\n",
    "                            hidden_size,\n",
    "                            num_layers,\n",
    "                            dropout=dropout,\n",
    "                            bidirectional=bidirectional)\n",
    "        self.linear = nn.Linear(hidden_size, input_size)\n",
    "        \n",
    "    def forward(self,inputs,hidden):\n",
    "        outputs,hidden=self.lstm(inputs,hidden)\n",
    "        predictions=self.linear(outputs[-1])\n",
    "        return predictions,outputs,hidden\n",
    "    \n",
    "    def init_hidden(self,batch_size):\n",
    "        num_directions=2 if self.bidirectional else 1\n",
    "        hidden = (torch.zeros(self.num_layers*num_directions, batch_size, self.hidden_size),\n",
    "                  torch.zeros(self.num_layers*num_directions, batch_size, self.hidden_size))\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_batch(x,y,i,batch_size):\n",
    "    if x.dim() == 2:\n",
    "        x = x.unsqueeze(2)\n",
    "    batch_x = x[(i*batch_size):(i*batch_size)+batch_size, :, :]\n",
    "    batch_y = y[(i*batch_size):(i*batch_size)+batch_size]\n",
    "\n",
    "    # Reshape Tensors into (seq_len, batch_size, input_size) format for the LSTM.\n",
    "    batch_x = batch_x.transpose(0, 1)\n",
    "    \n",
    "    return batch_x, batch_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model,x_train_set,y_train_set,optimizer,batch_size,epoch):\n",
    "    num_sequences=x_train_set.size(0)\n",
    "    num_batches=num_sequences//batch_size\n",
    "    \n",
    "    total_loss=0\n",
    "    \n",
    "    model.train()\n",
    "    for i in range(num_batches):\n",
    "        # Get input and target batches and reshape for LSTM.\n",
    "        batch_x, batch_y = get_batch(x_train_set, y_train_set, i, batch_size)\n",
    "\n",
    "        # Reset the gradient.\n",
    "        lstm.zero_grad()\n",
    "        \n",
    "        # Initialize the hidden states (see the function lstm.init_hidden(batch_size)).\n",
    "        hidden = lstm.init_hidden(batch_size)\n",
    "        \n",
    "        # Complete a forward pass.\n",
    "        y_pred, outputs, hidden = lstm(batch_x,hidden)\n",
    "        \n",
    "        # Calculate the loss with the 'loss_fn'.\n",
    "        loss = loss_fn(y_pred,batch_y)\n",
    "        \n",
    "        # Compute the gradient.\n",
    "        loss.backward()\n",
    "        \n",
    "        # Clip to the gradient to avoid exploding gradient.\n",
    "        nn.utils.clip_grad_norm_(lstm.parameters(), max_grad_norm)\n",
    "\n",
    "        # Make one step with optimizer.\n",
    "        optimizer.step()\n",
    "        \n",
    "        # Accumulate the total loss.\n",
    "        total_loss += loss.data\n",
    "        \n",
    "    print(\"Epoch {}: Loss = {:.8f}\".format(epoch+1, total_loss/num_batches))\n",
    "    return total_loss/num_batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval(model,x_valid_set,y_valid_set,optimizer,batch_size):\n",
    "    num_sequences=x_valid_set.size(0)\n",
    "    num_batches=num_sequences//batch_size\n",
    "    \n",
    "    total_loss=0\n",
    "    \n",
    "    model.eval()\n",
    "    for i in range(num_batches):\n",
    "        # Get input and target batches and reshape for LSTM.\n",
    "        batch_x, batch_y = get_batch(x_valid_set, y_valid_set, i, batch_size)\n",
    "\n",
    "        # Reset the gradient.\n",
    "        lstm.zero_grad()\n",
    "        \n",
    "        # Initialize the hidden states (see the function lstm.init_hidden(batch_size)).\n",
    "        hidden = lstm.init_hidden(batch_size)\n",
    "        \n",
    "        # Complete a forward pass.\n",
    "        y_pred, outputs, hidden = lstm(batch_x,hidden)\n",
    "        \n",
    "        pred=y_pred*(max_value.sub(min_value))+(min_value)\n",
    "        \n",
    "        # Calculate the loss with the 'loss_fn'.\n",
    "        loss = loss_fn(y_pred,batch_y)\n",
    "        \n",
    "        # Compute the gradient.\n",
    "        loss.backward()\n",
    "        \n",
    "        # Clip to the gradient to avoid exploding gradient.\n",
    "        nn.utils.clip_grad_norm_(lstm.parameters(), max_grad_norm)\n",
    "\n",
    "        # Make one step with optimizer.\n",
    "        optimizer.step()\n",
    "        \n",
    "        # Accumulate the total loss.\n",
    "        total_loss += loss.data\n",
    "\n",
    "    print(\"Validation: Loss = {:.8f}\".format(total_loss/num_batches))\n",
    "    return total_loss/num_batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_model(epoch, model, path='./'):\n",
    "    \n",
    "    # file name and path \n",
    "    filename = path + 'montreal.pt'\n",
    "    \n",
    "    # load the model parameters \n",
    "    torch.save(model.state_dict(), filename)\n",
    "    \n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model for 30 epoch\n",
      "Training model for 30 epoch\n",
      "Epoch 1: Loss = 0.09246875\n",
      "Epoch 1: Loss = 0.09246875\n",
      "Validation: Loss = 0.05351315\n",
      "Validation: Loss = 0.05351315\n",
      "Epoch 2: Loss = 0.04625485\n",
      "Epoch 2: Loss = 0.04625485\n",
      "Validation: Loss = 0.03898960\n",
      "Validation: Loss = 0.03898960\n",
      "Epoch 3: Loss = 0.03589375\n",
      "Epoch 3: Loss = 0.03589375\n",
      "Validation: Loss = 0.03264852\n",
      "Validation: Loss = 0.03264852\n",
      "Epoch 4: Loss = 0.03094397\n",
      "Epoch 4: Loss = 0.03094397\n",
      "Validation: Loss = 0.02930296\n",
      "Validation: Loss = 0.02930296\n",
      "Epoch 5: Loss = 0.02867482\n",
      "Epoch 5: Loss = 0.02867482\n",
      "Validation: Loss = 0.02777198\n",
      "Validation: Loss = 0.02777198\n",
      "Epoch 6: Loss = 0.02744476\n",
      "Epoch 6: Loss = 0.02744476\n",
      "Validation: Loss = 0.02689401\n",
      "Validation: Loss = 0.02689401\n",
      "Epoch 7: Loss = 0.02668482\n",
      "Epoch 7: Loss = 0.02668482\n",
      "Validation: Loss = 0.02630113\n",
      "Validation: Loss = 0.02630113\n",
      "Epoch 8: Loss = 0.02614713\n",
      "Epoch 8: Loss = 0.02614713\n",
      "Validation: Loss = 0.02580957\n",
      "Validation: Loss = 0.02580957\n",
      "Epoch 9: Loss = 0.02567809\n",
      "Epoch 9: Loss = 0.02567809\n",
      "Validation: Loss = 0.02536530\n",
      "Validation: Loss = 0.02536530\n",
      "Epoch 10: Loss = 0.02530378\n",
      "Epoch 10: Loss = 0.02530378\n",
      "Validation: Loss = 0.02503494\n",
      "Validation: Loss = 0.02503494\n",
      "Epoch 11: Loss = 0.02505226\n",
      "Epoch 11: Loss = 0.02505226\n",
      "Validation: Loss = 0.02480734\n",
      "Validation: Loss = 0.02480734\n",
      "Epoch 12: Loss = 0.02487141\n",
      "Epoch 12: Loss = 0.02487141\n",
      "Validation: Loss = 0.02463250\n",
      "Validation: Loss = 0.02463250\n",
      "Epoch 13: Loss = 0.02472360\n",
      "Epoch 13: Loss = 0.02472360\n",
      "Validation: Loss = 0.02448623\n",
      "Validation: Loss = 0.02448623\n",
      "Epoch 14: Loss = 0.02459444\n",
      "Epoch 14: Loss = 0.02459444\n",
      "Validation: Loss = 0.02435742\n",
      "Validation: Loss = 0.02435742\n",
      "Epoch 15: Loss = 0.02447745\n",
      "Epoch 15: Loss = 0.02447745\n",
      "Validation: Loss = 0.02423951\n",
      "Validation: Loss = 0.02423951\n",
      "Epoch 16: Loss = 0.02436917\n",
      "Epoch 16: Loss = 0.02436917\n",
      "Validation: Loss = 0.02412838\n",
      "Validation: Loss = 0.02412838\n",
      "Epoch 17: Loss = 0.02426726\n",
      "Epoch 17: Loss = 0.02426726\n",
      "Validation: Loss = 0.02402166\n",
      "Validation: Loss = 0.02402166\n",
      "Epoch 18: Loss = 0.02417045\n",
      "Epoch 18: Loss = 0.02417045\n",
      "Validation: Loss = 0.02391824\n",
      "Validation: Loss = 0.02391824\n",
      "Epoch 19: Loss = 0.02407786\n",
      "Epoch 19: Loss = 0.02407786\n",
      "Validation: Loss = 0.02381800\n",
      "Validation: Loss = 0.02381800\n",
      "Epoch 20: Loss = 0.02398896\n",
      "Epoch 20: Loss = 0.02398896\n",
      "Validation: Loss = 0.02372145\n",
      "Validation: Loss = 0.02372145\n",
      "Epoch 21: Loss = 0.02390325\n",
      "Epoch 21: Loss = 0.02390325\n",
      "Validation: Loss = 0.02362916\n",
      "Validation: Loss = 0.02362916\n",
      "Epoch 22: Loss = 0.02382020\n",
      "Epoch 22: Loss = 0.02382020\n",
      "Validation: Loss = 0.02354138\n",
      "Validation: Loss = 0.02354138\n",
      "Epoch 23: Loss = 0.02373930\n",
      "Epoch 23: Loss = 0.02373930\n",
      "Validation: Loss = 0.02345803\n",
      "Validation: Loss = 0.02345803\n",
      "Epoch 24: Loss = 0.02366013\n",
      "Epoch 24: Loss = 0.02366013\n",
      "Validation: Loss = 0.02337891\n",
      "Validation: Loss = 0.02337891\n",
      "Epoch 25: Loss = 0.02358254\n",
      "Epoch 25: Loss = 0.02358254\n",
      "Validation: Loss = 0.02330385\n",
      "Validation: Loss = 0.02330385\n",
      "Epoch 26: Loss = 0.02350663\n",
      "Epoch 26: Loss = 0.02350663\n",
      "Validation: Loss = 0.02323272\n",
      "Validation: Loss = 0.02323272\n",
      "Epoch 27: Loss = 0.02343271\n",
      "Epoch 27: Loss = 0.02343271\n",
      "Validation: Loss = 0.02316520\n",
      "Validation: Loss = 0.02316520\n",
      "Epoch 28: Loss = 0.02336104\n",
      "Epoch 28: Loss = 0.02336104\n",
      "Validation: Loss = 0.02310066\n",
      "Validation: Loss = 0.02310066\n",
      "Epoch 29: Loss = 0.02329159\n",
      "Epoch 29: Loss = 0.02329159\n",
      "Validation: Loss = 0.02303804\n",
      "Validation: Loss = 0.02303804\n",
      "Epoch 30: Loss = 0.02322400\n",
      "Epoch 30: Loss = 0.02322400\n",
      "Validation: Loss = 0.02297573\n",
      "\n",
      "\n",
      "\n",
      "Optimization ended.\n",
      "\n",
      "Validation: Loss = 0.02297573\n",
      "\n",
      "\n",
      "\n",
      "Optimization ended.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_size=13\n",
    "hidden_size=24\n",
    "num_layers=2\n",
    "lstm=LSTM(input_size,hidden_size)\n",
    "\n",
    "learning_rate=0.001\n",
    "max_grad_norm=5\n",
    "loss_fn = nn.MSELoss()\n",
    "optimizer = optim.Adam(lstm.parameters(), lr=learning_rate,weight_decay=0.00001)\n",
    "\n",
    "batch_size = 8\n",
    "num_epochs = 30 #3\n",
    "# num_sequences = x_train_set.size(0)\n",
    "# num_batches = num_sequences //batch_size\n",
    "\n",
    "checkpoint_freq = 10\n",
    "path = './'\n",
    "\n",
    "train_losses=[]\n",
    "valid_losses=[]\n",
    "\n",
    "print(\"Training model for {} epoch\".format(num_epochs))\n",
    "for epoch in range(num_epochs):\n",
    "    total_loss = 0\n",
    "\n",
    "    # Shuffle input and target sequences.\n",
    "    idx = torch.randperm(x_train_set.size(0))\n",
    "    x = x_train_set[idx]\n",
    "    y = y_train_set[idx]\n",
    "\n",
    "    train_loss=train(lstm,x_train_set,y_train_set,optimizer,batch_size,epoch)\n",
    "    valid_loss=eval(lstm,x_valid_set,y_valid_set,optimizer,batch_size)\n",
    "    \n",
    "    train_losses.append(train_loss)\n",
    "    valid_losses.append(valid_loss)\n",
    "    \n",
    "    # Checkpoint\n",
    "    if epoch % checkpoint_freq ==0:\n",
    "        save_model(epoch, lstm, path)\n",
    "        \n",
    "# Last checkpoint\n",
    "save_model(num_epochs, lstm, path)\n",
    "    \n",
    "print(\"\\n\\n\\nOptimization ended.\\n\")\n",
    "\n",
    "plt.plot(train_losses, color=\"darkcyan\", label=\"train\")\n",
    "plt.plot(valid_losses, color=\"tomato\",label=\"validation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_one_hour(model,x_valid_set,input_size,num_steps):\n",
    "    predictions=torch.zeros(num_steps)\n",
    "    for i, x in enumerate(x_valid_set):\n",
    "        hidden=model.init_hidden(1)\n",
    "        y_pred,_,_=model(x.contiguous().view(-1, 1, input_size),hidden)\n",
    "#         predictions[i]=y_pred[:,-1]*(max_value[-1]-min_value[-1])+min_value[-1]\n",
    "        predictions[i]=y_pred[:,-1]\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_step_predictions = predict_one_hour(lstm, x_valid_set, input_size, y_valid_set.size(0))\n",
    "plt.plot(y_valid_set[:,-1].data.numpy(),color='darkcyan')\n",
    "plt.plot(one_step_predictions.data.numpy(),color='tomato')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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